普适的Dzyaloshinski-Moriya相互作用决定了非常规超导体家族中的配对
Universal Dzyaloshinski-Moriya interaction dictates pairing in unconventional superconductor families
- CIC nanoGUNE BRTA（巴斯克研究中心纳米GUNE）
- Beijing Computational Science Research Center（北京计算科学研究中心）
- Zhejiang University（浙江大学）
- Beijing Normal University（北京师范大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文通过第一性原理和DMRG模拟，揭示Dzyaloshinski-Moriya相互作用是铜氧化物、铁基和镍酸盐超导体的共同要素，统一非共线磁性，并指出空穴条纹-涡旋耦合是微观配对引擎，挑战现有配对机制。
AI中文摘要：
共线反铁磁自旋涨落范式长期以来指导着非常规超导研究，但无法调和在铜氧化物、铁基超导体和镍酸盐中观察到的非共线自旋现象。通过广泛的第一性原理计算和无偏的大规模DMRG模拟，我们表明Dzyaloshinski-Moriya相互作用（DMI）——源于局域反演对称性破缺——是这些家族中的共同要素。该DMI统一了母体化合物中的标志性观测结果——非公度序、自旋波隙和非共线织构。在空穴掺杂下，强DMI驱动自旋涡旋与π移位的空穴条纹合并，形成混合涡旋-空穴条纹相。这些相稳定电荷序，同时支持而非抑制超导性。相比之下，在电子掺杂下，这些涡旋钉扎空穴并抑制长程超导性。我们的结果确立了DMI作为非共线磁性与超导性之间的统一联系，将空穴条纹-涡旋耦合确定为微观配对引擎。鉴于DMI在主要超导体家族中普遍存在，这些发现挑战了现有的配对机制，并为材料优化提供了实验上可检验的路线图。
英文摘要：
The collinear-antiferromagnetic spin-fluctuation paradigm has long guided unconventional superconductivity research, yet fails to reconcile the noncollinear spin phenomena observed across cuprates, iron-based superconductors, and nickelates. Using extensive first-principles calculations and unbiased large-scale DMRG simulations, we show that Dzyaloshinski-Moriya interaction (DMI)-arising from local inversion-symmetry breaking-is a common ingredient across these families. This DMI unifies hallmark observations in parent compounds-incommensurate orders, spin-wave gaps, and noncollinear textures. Under hole doping, strong DMI drives spin vortices to merge with pi-shifted hole stripes, forming hybrid vortex-hole stripe phases. These phases stabilize charge order while supporting, not suppressing, superconductivity. By contrast, under electron doping, these vortices pin holes and suppress long-range superconductivity. Our results establish DMI as a unifying link between noncollinear magnetism and superconductivity, identifying hole-strip-vortex coupling as a microscopic pairing engine. Given that DMI is common across major superconductor families, these findings challenge the prevailing pairing mechanism and offer an experimentally testable roadmap for materials optimization.
